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Raspberry Pi Computer Vision Programming

Raspberry Pi Computer Vision Programming

By : Ashwin Pajankar
3.8 (4)
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Raspberry Pi Computer Vision Programming

Raspberry Pi Computer Vision Programming

3.8 (4)
By: Ashwin Pajankar

Overview of this book

Raspberry Pi is one of the popular single-board computers of our generation. All the major image processing and computer vision algorithms and operations can be implemented easily with OpenCV on Raspberry Pi. This updated second edition is packed with cutting-edge examples and new topics, and covers the latest versions of key technologies such as Python 3, Raspberry Pi, and OpenCV. This book will equip you with the skills required to successfully design and implement your own OpenCV, Raspberry Pi, and Python-based computer vision projects. At the start, you'll learn the basics of Python 3, and the fundamentals of single-board computers and NumPy. Next, you'll discover how to install OpenCV 4 for Python 3 on Raspberry Pi, before covering major techniques and algorithms in image processing, manipulation, and computer vision. By working through the steps in each chapter, you'll understand essential OpenCV features. Later sections will take you through creating graphical user interface (GUI) apps with GPIO and OpenCV. You'll also learn to use the new computer vision library, Mahotas, to perform various image processing operations. Finally, you'll explore the Jupyter Notebook and how to set up a Windows computer and Ubuntu for computer vision. By the end of this book, you'll be able to confidently build and deploy computer vision apps.
Table of Contents (15 chapters)
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Working with images using OpenCV

In this section, we will learn to read and store images using the OpenCV API and Python. All the programs in this book will use the OpenCV library. It can be imported with the following Python 3 statement:

import cv2

The cv2.imread() function reads an image from the disk and stores it in a NumPy ndarray. It accepts two arguments. The first argument is the name of the image file on the disk. The image should either be in the same directory where we are saving the current Python 3 script, or we must pass the absolute path of the image file as an argument to the cv2.imread() function.

The second argument is a flag that specifies the mode in which the image should be read. The flag can have one of the following values:

  • cv2.IMREAD_GRAYSCALE: This reads an image from the disk in grayscale mode. The numerical value corresponding to this flag is 0.
  • cv2.IMREAD_UNCHANGED: This reads an image from the disk as it is. The numerical value corresponding...

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